This paper investigates an ensemble framework which is proposed for accurate classification of hyperspectral data. The usefulness of the method, designed to be a simple and robust supervised classification tool, is assessed on real data, characterized by classes with very similar spectral responses, and limited amount of ground truth labeled training samples. The method is inspired by the framework of the Random Forests method proposed by Breiman (2001). The success of the method relies on the use of support vector machines (SVMs) as base classifiers, the freedom of random selection of input features to create diversity in the ensemble, and the use of the weighted majority voting scheme to combine classification results. Although not fully optimized, a simple and feasible solution is adopted for tuning the SVM parameters of the base classifiers, aiming its use in practical applications. Moreover, the effect of an additional pre-processing module for an initial feature reduction is investigated. Encouraging results suggest the proposed method as promising, in addition to being easy to implement.

Date Published: 24 October 2007
PDF: 12 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480P (24 October 2007); doi: 10.1117/12.738308

Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)